ODIN AD: a framework supporting the life-cycle of time series anomaly detection applications

Anomaly detection (AD) in numerical temporal data series is a prominent task in many domains, including the analysis of industrial equipment operation, the processing of IoT data streams, and the monitoring of appliance energy consumption. The life-cycle of an AD application with a Machine Learning (ML) approach requires data collection and preparation, algorithm design and selection, training, and evaluation. All these activities contain repetitive tasks which could be supported by tools. This paper describes ODIN AD, a framework assisting the life-cycle of AD applications in the phases of data preparation, prediction performance evaluation, and error diagnosis

Proactive Buildings: A Prescriptive Maintenance Approach

Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings

Black-box error diagnosis in Deep Neural Networks for computer vision: a survey of tools

The application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for coping with the complex and opaque nature of these architectures. When a gold standard is available, performance assessment treats the DNN as a black box and computes standard metrics based on the comparison of the predictions with the ground truth. A deeper understanding of performances requires going beyond such evaluation metrics to diagnose the model behavior and the prediction errors. This goal can be pursued in two complementary ways. On one side, model interpretation techniques "open the box " and assess the relationship between the input, the inner layers and the output, so as to identify the architecture modules most likely to cause the performance loss. On the other hand, black-box error diagnosis techniques study the correlation between the model response and some properties of the input not used for training, so as to identify the features of the inputs that make the model fail. Both approaches give hints on how to improve the architecture and/or the training process. This paper focuses on the application of DNNs to computer vision (CV) tasks and presents a survey of the tools that support the black-box performance diagnosis paradigm. It illustrates the features and gaps of the current proposals, discusses the relevant research directions and provides a brief overview of the diagnosis tools in sectors other than CV

Anhydrosugars in aerosol samples from Ny-Alesund, Svalbard Island, 2014

Anhydrosugars were quantified in 16 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2014. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Anhydrosugar content was measured using IC-MS

Sugars in aerosol samples from Ny-Alesund, Svalbard Island, 2014

Sugars were quantified in 16 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2014. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Sugar content was measured using IC-MS

Water soluble compounds in size-segregated Arctic aerosol at Gruvebadet, Ny-Ålesund in 2013, 2014, 2015 and 2018-2019

Water Soluble Organic Compounds (WSOCs) chemistry in the Arctic region occurs through several sources, depending on the considered compound. We examine here L- & D- Free and Combined amino acids, mono-saccharides, di-saccharides, anhydro-sugars, alcohol-sugars, organic acids and major cations and anions in 2013, 2014, 2015 and 2018 Arctic campaign at Gruvebadet Laboratory, close to Ny-Ålesund (Svalbard Island - 78°55′03″N, 11°53′39″E). A multi-stage Andersen impactor (TE-6000 series, Tisch Environmental Inc.) was used to collect aerosol samples on six pre-combusted (4 h at 400 °C in a muffle furnace) quartz fiber filters. The sampler accumulated particles with a cut-off diameters of 10.0 μm, 7.2 μm, 3.0 μm, 1.5 μm, and 0.95 μm on slotted filters and <0.49 μm on backup filter. The frequency of sampling varied from 1 to 10 days with a total air volume of about 1000 or 15000 m³ per sample

Ions and organc acids in aerosol samples from Ny-Alesund, Svalbard Island, 2018 - 2019

Ions and organic acids were quantified in 46 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2018 and 2019. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Ion and organic acid content was measured using IC-MS and HPLC-MS

Ions and organc acids in aerosol samples from Ny-Alesund, Svalbard Island, 2015

Ions and organic acids were quantified in 7 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2015. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Ion and organic acid content was measured using IC-MS and HPLC-MS

Ions and organc acids in aerosol samples from Ny-Alesund, Svalbard Island, 2014

Ions and organic acids were quantified in 16 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2014. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Ion and organic acid content was measured using IC-MS and HPLC-MS

Anhydrosugars in aerosol samples from Ny-Alesund, Svalbard Island, 2018 - 2019

Anhydrosugars were quantified in 46 aerosol samples collected during the Arctic campaign at Gruvebadet Laboratory in 2018 and 2019. Aerosol samples were collected in quartz fiber filters. They were spiked with isotopically labelled standard solutions (2 - 3 µg/ml) prior extraction. Anhydrosugar content was measured using IC-MS